Information Sciences Institute

USC ISI Lab Seeks to Unravel Quantum Dimensions of Light

When we think of information, we think of bits and bytes, computer algorithms and communication networks. But information is physical, stored on physical things like electrons, RF radiation, even water and light.

Want to Know the Secret of a Good Idea? It Could be Hidden in Language.

What makes a good idea, good? Why do some innovations—like the touchscreen—thrive, while others fall into obscurity? Remember the minidisc, anyone?

ISI Spotlight: Fred Morstatter, Computer Scientist

Predicting the next geopolitical event—from who will be the next Pope to who will win the national election in India—is no easy task. But ISI Computer Scientist Fred Morstatter is up for the challenge.

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ISI Researcher's Machine Learning Method Unearths Early Signs of Alzheimer’s

January 28, 2019

Nearly 50 million people worldwide have Alzheimer’s disease or another form of dementia. While age is the greatest risk factor for developing the disease, researchers believe most Alzheimer’s cases occur as a result of complex interactions among genes and other factors. But those factors and the role they play are not known—yet.

In a new study, USC researchers used machine learning to identify potential blood-based markers of Alzheimer’s disease that could help with earlier diagnosis and lead to non-invasive ways of tracking the progress of the disease in patients.

The method was developed by USC computer science research assistant professor Greg Ver Steeg, a senior research lead at the USC Information Sciences Institute (ISI). Machine learning is a subset of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed.

“This type of analysis is a novel way of discovering patterns of data to identify key diagnostic markers of disease,” said team member Paul Thompson, the associate director of the USC Mark and Mary Stevens Neuroimaging and Informatics Institute and professor in the Keck School of Medicine at USC. “In a very large database of health measures, it helped us discover predictive features of Alzheimer’s disease that nobody suspected were there.”

The study, “Uncovering Biologically Coherent Peripheral Signatures of Health and Risk for Alzheimer’s Disease in the Aging Brain,” appeared in Frontiers in Aging Neuroscience, Nov. 28. The study authors are from the USC Mark and Mary Stevens Neuroimaging and Informatics Institute and the USC Information Sciences Institute.